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Modeling a wind turbine sound field involves taking into account the main aeroacoustic sources that are generally dominant for modern wind turbines, as well as environmental phenomena such as atmospheric conditions and ground properties that are variable in both time and space. A crucial step to obtain reliable predictions is to estimate the relative influence of environmental parameters on acoustic emission and propagation, in order to determine the parameters that induce the greatest variability on sound pressure level. Thus, this study proposes a Morris sensitivity analysis of a wind turbine noise emission model combined with a sound propagation model in downwind conditions. The emission model is based on Amiet's theory and propagation effects are modeled by the wide-angle parabolic equation. The whole simulation takes into account ground effects (absorption through acoustic impedance and scattering through surface roughness) and micrometeorological effects (mean refraction through the vertical gradient of effective sound speed). The final results show that the parameters involved in atmospheric refraction and in ground absorption have a significant influence on sound pressure level. On the other hand, in the context of this study the relative air humidity and the ground roughness parameters appear to be negligible on sound pressure level sensitivity.
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http://dx.doi.org/10.1121/10.0002872 | DOI Listing |
PLoS One
September 2025
Electrical Engineering Determent, Faculty of Engineering, Minia University, Minia, Egypt.
Renewable energy systems are at the core of global efforts to reduce greenhouse gas (GHG) emissions and to combat climate change. Focusing on the role of energy storage in enhancing dependability and efficiency, this paper investigates the design and optimization of a completely sustainable hybrid energy system. Furthermore, hybrid storage systems have been used to evaluate their viability and cost-benefits.
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September 2025
School of Civil Engineering, Shandong Jianzhu University, Jinan, China.
In engineering structure performance monitoring, capturing real-time on-site data and conducting precise analysis are critical for assessing structural condition and safety. However, equipment instability and complex on-site environments often lead to data anomalies and gaps, hindering accurate performance evaluation. This study, conducted within a wind farm reinforcement project in Shandong Province, addresses these challenges by focusing on anomaly detection and data imputation for weld nail strain, anchor cable axial force, and concrete strain.
View Article and Find Full Text PDFAntimicrob Steward Healthc Epidemiol
August 2025
School of Medicine, Stanford University, Palo Alto, CA, USA.
Objective: To evaluate the effectiveness and acceptability of ventilation interventions in naturally ventilated hospitals in Liberia.
Design: Difference-in-differences analysis of pre- and post-air changes per hour of intervention and control spaces.
Setting: Hospitals in Bong and Montserrado Counties, Liberia.
Waste Manag Res
September 2025
School of Chemical Engineering and Technology, China University of Mining and Technology, Xuzhou, Jiangsu, China.
This study investigates the application of triboelectric separation technology for the efficient recovery of glass fibre-reinforced polymers (GFRPs) from wind turbine blade. Through systematic experiments, the effects of friction materials, electrode voltage and feed rate on separation efficiency were evaluated. The results demonstrate that using polymethyl methacrylate as the friction material, with an electrode voltage of 12.
View Article and Find Full Text PDFSci Data
September 2025
CRRC Zhuzhou Times New Material Technology Co., Ltd., Zhuzhou, China.
Lightning strikes pose a significant threat to the structural integrity and operational performance of wind turbine blades. Due to the high probability of lightning strikes but the difficulty in capturing their dynamic data, obtaining comprehensive data on blades subjected to lightning strikes is challenging. This study presents a rare multimodal dataset for wind turbine blade monitoring during lightning strikes (MDWTBM-LS).
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